Abstract
This paper presents EMGSense, a low-effort self-supervised domain adaptation framework for sensing applications based on Electromyography (EMG). EMGSense addresses one of the fundamental challenges in EMG cross-user sensing—the significant performance degradation caused by time-varying biological heterogeneity—in a low-effort (data-efficient and label-free) manner. To alleviate the burden of data collection and avoid labor-intensive data annotation, we propose two EMG-specific data augmentation methods to simulate the EMG signals generated in various conditions and scope the exploration in label-free scenarios. We model combating biological heterogeneity-caused performance degradation as a multi-source domain adaptation problem that can learn from the diversity among source users to eliminate EMG heterogeneous biological features. To relearn the target-user-specific biological features from the unlabeled data, we integrate advanced self-supervised techniques into a carefully designed deep neural network (DNN) structure. The DNN structure can seamlessly perform two training stages that complement each other to adapt to a new user with satisfactory performance. Comprehensive evaluations on two sizable datasets collected from 13 participants indicate that EMGSense achieves an average accuracy of 91.9% and 81.2% in gesture recognition and activity recognition, respectively. EMGSense outperforms the state-of-the-art EMG-oriented domain adaptation approaches by 12.5%-17.4% and achieves a comparable performance with the one trained in a supervised learning manner.
Original language | English |
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Title of host publication | Proceedings of the 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom) |
Place of Publication | Danvers |
Publisher | IEEE |
Pages | 160-170 |
Number of pages | 11 |
ISBN (Electronic) | 978-1-6654-5378-3 |
ISBN (Print) | 978-1-6654-5379-0 |
DOIs | |
Publication status | Published - 2023 |
Event | 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom) - Atlanta, United States Duration: 13 Mar 2023 → 17 Mar 2023 |
Publication series
Name | 2023 IEEE International Conference on Pervasive Computing and Communications, PerCom 2023 |
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Conference
Conference | 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom) |
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Country/Territory | United States |
City | Atlanta |
Period | 13/03/23 → 17/03/23 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- EMG sensing
- biological heterogeneity
- domain adaptation
- self-supervised learning